---
sourceDocument: Australia Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/intelligent-experiences

 Release :

    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Extraction methods

# Content extraction methods {#ariaid-title1}

Release version: Australia  
Updated November 5, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Content extraction methods

The Extract information from documents skill in ServiceNow supports three AI-assisted content extraction methods---field, table, and Q\&A extraction---designed to capture and store various types of document data effectively.
These methods enable you to parse documents like invoices, purchase orders, and contracts, and populate corresponding records in ServiceNow tables.
Show full answer Show less  

## Extraction Methods and Their Use Cases

* **Field extraction:** Captures specific, isolated data points that appear once per document, mapping each to a single ServiceNow record field. Ideal for structured data such as invoice numbers, vendor names, dates, monetary amounts, and addresses. Related fields can be grouped logically (e.g., Billing Address).
* **Table extraction:** Extracts multi-row, dynamic lists of related data entries where the number of items varies per document. Suitable for invoice line items, expense report details, parts lists, or multi-page itineraries, capturing them as grids of rows and columns.
* **Q\&A extraction:** Uses generative AI to interpret and answer open-ended questions from unstructured narrative text that doesn't fit fixed schemas. Best for extracting contextual information like contract clauses, reasons for termination, or special terms. Note: Outputs require review due to possible inaccuracies.

## Choosing the Right Extraction Method

Select the method based on the data type and structure in your documents:

* **Field extraction:** For single, well-defined data points (numbers, dates, names).
* **Table extraction:** For repeating lists or grids with variable row counts.
* **Q\&A extraction:** For narrative or interpretive information embedded in text.

You can combine these methods within a single use case to comprehensively extract all relevant data types from your documents.  
The Extract information from documents skill (Information Extraction skill) supports three extraction methods---field, table, and Q\&A---each suited for different types of document data. Understanding which method to use helps
you capture and store the right information in ServiceNow.

The Information Extraction skill uses AI-assisted parsing to extract data from documents (invoices, purchase orders, and contracts) and then populates records in ServiceNow. Select a method or
combination of methods that matches the type of information you want to capture and store in ServiceNow tables.{#content-extraction-methods__data-extraction-methods-para-1}

## Field extraction {#content-extraction-methods__data-extraction-methods-field}

Field extraction captures specific, isolated data points from a document---values that appear exactly once---and maps each to a single field on a ServiceNow record.{#content-extraction-methods__data-extraction-methods-field-para-1}

Use field extraction for data such as:{#content-extraction-methods__data-extraction-methods-field-para-2}

* Invoice numbers, purchase order numbers, or document identifiers
* Vendor or customer names
* Dates, such as an invoice date, due date, or contract start date
* Monetary totals or tax amounts
* Addresses or other contact details

{#content-extraction-methods__ul_ztx_z31_ckc}

You can bundle related single fields into a logical group. For example, a Billing Address group can capture Street, City, State, and ZIP from the same region of a document.{#content-extraction-methods__data-extraction-methods-field-para-3}

## Table extraction {#content-extraction-methods__data-extraction-methods-table}

Table extraction captures dynamic, multi-row data from documents where the number of entries varies between documents, such as invoice line items or expense report details. A table acts as a
two-dimensional grid of rows and columns that captures a list of related records.{#content-extraction-methods__data-extraction-methods-table-para-1}

Use table extraction for data such as:{#content-extraction-methods__data-extraction-methods-table-para-2}

* Invoice or purchase order line items (quantity, description, unit price, total)
* Expense report breakdowns
* Parts lists or bills of materials
* Multi-page flight itineraries or timesheet entries
{#content-extraction-methods__ul_a5x_z31_ckc}

## Q\&A extraction {#content-extraction-methods__data-extraction-methods-qa}

Q\&A extraction uses generative AI to answer open-ended, natural language questions about the contents of a document. It is suited for unstructured text that does not fit neatly into a
predefined field or table schema. The extraction processes meaning rather than matching a fixed pattern.{#content-extraction-methods__data-extraction-methods-qa-para-1}  
Warning:  
Q\&A extraction uses generative AI and may produce inaccurate or incomplete results. Review all AI-generated output before using it to populate records.

Use Q\&A extraction for questions such as:{#content-extraction-methods__data-extraction-methods-qa-para-2}

* The reason for termination
* Special shipping or delivery terms
* The designated project manager
* Narrative clauses or conditional language from contracts

{#content-extraction-methods__ul_b5x_z31_ckc}

You can also use Q\&A extraction to categorize documents.{#content-extraction-methods__data-extraction-methods-qa-para-3}

## Selecting an extraction method {#content-extraction-methods__data-extraction-methods-select}

Use the following guidance to choose the appropriate extraction method.{#content-extraction-methods__data-extraction-methods-select-para-1}
{#content-extraction-methods__table_tx1_p3v_wjc__entry__3}

| Extraction method | When to use | Example |
|-|-|-|
| Field extraction | Use when the data point appears once and has a well-defined type. | A number, date, or name. |
| Table extraction | Use when the document contains a list or grid of repeating items where the row count is unknown or varies across documents. | Line items in an invoice or purchase order. |
| Q\&A extraction | Use when the information is embedded in narrative text and requires interpretation rather than pattern matching. | An answer based on contract terms or policy text. |
[Table 1. Extraction method selection guidance]

{#content-extraction-methods__table_tx1_p3v_wjc}

You can combine multiple methods within a single use case to handle all of the data types present in a document.{#content-extraction-methods__data-extraction-methods-select-para-2}
**Related concepts**   

* [Use cases for the Information Extraction skill](https://www.servicenow.com/docs/y~Jz2L23f5ttfxiuSfS~7Q "Use cases in the Information Extraction skill define what documents to process and what information to extract, so generative AI can accurately retrieve structured data from unstructured documents.")